Speech quality assessment using EEG signals

Omri Bar, Ilan D. Shallom

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The listener experience in modern audio communication systems acts as a key indicator for the entire system quality. Consequently, speech quality assessment attracts great interest from both industry and academia. Common methods for speech quality assessment are either subjective or based on subjective experiments and therefore limited in their ability to produce unbiased results. In this paper, we introduce a new EEG-based distortion measure (EBDM) for speech quality assessment. The EEG signals are represented by multi-channel autoregressive parameters. These parameters are then used, in coordination with Dynamic Time Warping algorithm to compare EEG responses of two signals with different speech quality. The method demonstrates promising preliminary indications for the possibility of using EEG signals as an objective basis for assessing speech quality degradation levels. We believe that further improvements will allow such EEG-based methods to be competitive with standard ITU-T recommended quality testing.

Original languageEnglish
Title of host publication2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781509021529
DOIs
StatePublished - 4 Jan 2017
Event2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 - Eilat, Israel
Duration: 16 Nov 201618 Nov 2016

Publication series

Name2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016

Conference

Conference2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
Country/TerritoryIsrael
CityEilat
Period16/11/1618/11/16

ASJC Scopus subject areas

  • Computer Science Applications
  • Hardware and Architecture
  • Artificial Intelligence
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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